All Issue

2026 Vol.35, Issue 3 Preview Page

Original Articles

Image-Based Quality Evaluation of Shipping Stage Tomato Seedlings from Commercial Nurseries

공정육묘장 출하단계 토마토 묘의 영상 기반 품질 평가

Gwang Yun Kim1
,
Goun Im2
,
Geonhyoung Kim1
,
Jun Gu Lee3456*
김 광윤1
,
임 고운2
,
김 건형1
,
이 준구3456*
1Graduate Student, Department of Horticulture, College of Agriculture & Life Sciences, Jeonbuk National University, Jeonju 54896, Korea
2Graduate Student, Department of AI in Agriculture, Graduate School of AI Convergence, Jeonbuk National University, Jeonju 54896, Korea
3Professor, Department of Horticulture, College of Agriculture & Life Sciences, Jeonbuk National University, Jeonju 54896, Korea
4Professor, Department of AI in Agriculture, Graduate School of AI Convergence, Jeonbuk National University, Jeonju 54896, Korea
5Professor, Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju 54896, Korea
6Professor, Institute of Agricultural Science & Technology, Jeonbuk National University, Jeonju 54896, Korea
1전북대학교 농업생명과학대학 원예학과 대학원생
2전북대학교 AI융합대학원 스마트농업AI융합학과 대학원생
3전북대학교 농업생명과학대학 원예학과 교수
4전북대학교 AI융합대학원 스마트농업AI융합학과 교수
5전북대학교 부설 지능형로봇연구소 교수
6전북대학교 농업과학기술연구소 교수
*Corresponding Author
31 July 2026. pp. 274-285
PDF XML Citation
Abstract

The growth of seedlings produced from commercial nurseries may be related to early growth after transplanting. Therefore, rapid and objective evaluation of seedlings at the shipping stage is important. However, seedling evaluation in commercial nurseries often depends on visual assessment or requires considerable labor to measure seedling growth, and non-destructive, quantitative evaluation methods are therefore needed. The aim of this study was to evaluate the applicability of image-based measurements of plant height and leaf area in shipping stage of tomato seedlings produced in commercial nurseries and to examine the possibility of rapid and non-destructive evaluation of shoot growth and seedling quality indices using these measurements. Shipping stage tomato seedlings were collected from nine commercial nurseries in Korea, and image-based plant height and leaf area were measured using a Plant Image Measurement System (PIMS). After destructive measurements, seedling growth was evaluated, and the compactness index (CI) and sturdiness quotient (SQ) were calculated. Shipping stage tomato seedlings showed variation among nurseries in 10 growth traits. Image-based plant height and leaf area showed positive linear relationships with the destructive measurements, with R2 values of 0.7524 and 0.6837, respectively. Regression models using image-based plant height and leaf area showed the highest R2 for shoot fresh weight (R2 = 0.642), while the R2 for shoot dry weight was 0.509. In contrast, the R2 values for secondary transformed indices SQ and CI were relatively low at 0.407 and 0.312, respectively. These results indicate that image-based plant height and leaf area can be used for the non-destructive evaluation of morphological characteristics and shoot growth in shipping stage tomato seedlings. However, estimation of CI and SQ related to dry matter accumulation and stem diameter may be needed to improve the evaluation accuracy of seedling quality indices.

Keywords
biomass
compactness index
image-based measurement
Solanum lycopersicum
sturdiness quotient

공정육묘장에서 출하되는 묘의 생육 상태는 정식 후 초기 생육과 관련될 수 있으므로, 출하단계 묘를 신속하고 객관적으로 평가하는 것이 중요하다. 그러나 실제 육묘 현장에서는 묘 평가가 육안 판단에 의존하거나 노동집약적인 생육 조사를 필요로 하므로, 비파괴적이고 정량적인 평가 방법이 요구된다. 본 연구는 공정육묘장에서 생산된 출하단계 토마토 묘를 대상으로 영상 기반 초장 및 엽면적 측정의 적용 가능성을 평가하고, 이를 이용한 지상부 생육 및 묘 품질지표의 신속 비파괴 평가 가능성을 검토하고자 수행되었다. 국내 공정육묘장 9곳에서 출하단계 토마토 묘를 수집하고 Plant Image Measurement System(PIMS)을 이용하여 영상 기반 초장과 엽면적을 측정하였다. 이후 파괴조사를 통해 생육을 조사하고, sturdiness quotient(SQ)와 compactness index(CI)를 산출하였다. 출하단계 토마토 묘는 육묘장에 따라 지상부 생육에서 다양한 변이를 나타냈다. 영상 기반 초장과 엽면적은 파괴 측정값과 각각 R2 = 0.7524와 0.6837의 양의 선형 관계를 나타냈다. 영상 기반 초장과 엽면적을 이용한 회귀모델의 설명력은 지상부 생체중에서 가장 높았으며(R2 = 0.642), 지상부 건물중은 R2 = 0.509로 나타났다. 반면 SQ와 CI의 설명력은 각각 R2 = 0.407과 0.312로 상대적으로 낮았다. 이러한 결과는 영상 기반 초장과 엽면적이 출하단계 토마토 묘의 형태적 생육과 지상부 생육을 비파괴적으로 평가하기 위한 기초 지표로 활용될 수 있음을 보여준다. 다만 복합 품질지표의 평가 정확도를 높이기 위해서는 경경 및 건물 축적 특성을 반영할 수 있는 추가적인 비파괴 형질의 적용이 필요할 것으로 판단된다.

키워드
건전도 지수
생체량
영상 기반 측정
충실도 지수
Solanum lycopersicum
References
1

Abebe AM, Kim Y, Kim J, Kim SL, Baek J (2023) Image-based high-throughput phenotyping in horticultural crops. Plants 12:2061. https://doi.org/10.3390/plants12102061

10.3390/plants1210206137653978PMC10222289
2

Almansa EM, Espín A, Chica RM, Lao MT (2014) Bioassimilation behaviour of tomato seedling cultivars under different sources of artificial light. Aust J Crop Sci 8:873-880. https://doi.org/10.3316/informit.477666034809822

10.3316/informit.477666034809822
3

An N, Palmer CM, Baker RL, Markelz RJC, Ta J, Covington MF, Maloof JN, Welch SM, Weinig C (2016) Plant high-throughput phenotyping using photogrammetry and imaging techniques ic 127:376-394. https://doi.org/10.1016/j.compag.2016.04.002

10.1016/j.compag.2016.04.002
4

Atchley WR, Gaskins CT, Anderson D (1976) Statistical properties of ratios. I. Empirical results. Syst Zool 25:137-148. https://doi.org/10.2307/2412740

10.2307/2412740
5

Baek MH, Kim IS, Jo JS (2025) Non-destructive estimation of leaf area, fresh weight, and dry weight in leafy vegetables using image analysis. J Bio-Env Con 34:485-495. https://doi.org/10.12791/KSBEC.2025.34.4.485

10.12791/KSBEC.2025.34.4.485
6

Ban S, Hong I, Kwack Y (2023) Prediction of growth and quality of Chinese cabbage seedlings cultivated in different plug cell sizes via analysis of image data using multispectral camera. Horticulturae 9:1288. https://doi.org/10.3390/horticulturae9121288

10.3390/horticulturae9121288
7

Bertin N, Gary C (1998) Short and long term fluctuations of the leaf mass per area of tomato plants—implications for growth models. Ann Bot 82:71-81. https://doi.org/10.1006/anbo.1998.0647

10.1006/anbo.1998.0647
8

Binotto AF, Lúcio ADC, Lopes SJ (2010) Correlations between growth variables and the Dickson quality index in forest seedlings. Cerne 16:457-464. https://doi.org/10.1590/S0104-77602010000400005

10.1590/S0104-77602010000400005
9

Bozokalfa MK (2008) Short communication. Irrigation temperature effects on seedling growth and transplant quality of tomato, pepper and eggplant. Span J Agric Res 6:120-124. https://doi.org/10.5424/sjar/2008061-5250

10.5424/sjar/2008061-5250
10

Chitwood DH, Kumar R, Ranjan A, Pelletier JM, Townsley BT, Ichihashi Y, Martinez CC, Zumstein K, Harada JJ, Maloof JN, Sinha NR (2015) Light-induced indeterminacy alters shade-avoiding tomato leaf morphology. Plant Physiol 169:2030-2047. https://doi.org/10.1104/pp.15.01229

10.1104/pp.15.0122926381315PMC4634097
11

Currey CJ, Torres AP, Lopez RG, Jacobs DF (2013) The quality index – a new tool for integrating quantitative measurements to assess quality of young floriculture plants. Acta Hortic 1000:385-392. https://doi.org/10.17660/ActaHortic.2013.1000.53

10.17660/ActaHortic.2013.1000.53
12

Dickson A, Leaf AL, Hosner JF (1960) Quality appraisal of white spruce and white pine seedling stock in nurseries. For Chron 36:10-13. https://doi.org/10.5558/tfc36010-1

10.5558/tfc36010-1
13

Easlon HM, Bloom AJ (2014) Easy leaf area: Automated digital image analysis for rapid and accurate measurement of leaf area. Appl Plant Sci 2:1400033. https://doi.org/10.3732/apps.1400033

10.3732/apps.140003325202639PMC4103476
14

Gallegos-Cedillo VM, Najera C, Signore A, Ochoa J, Gallegos J, Egea-Gilabert C, Gruda NS, Fernandez JA (2024) Analysis of global research on vegetable seedlings and transplants and their impacts on product quality. J Sci Food Agric 104:4950-4965. https://doi.org/10.1002/jsfa.13309

10.1002/jsfa.13309
15

Geneve RL, Kester ST (2001) Evaluation of seedling size following germination using computer-aided analysis of digital images from a flat-bed scanner. HortScience 36:1117-1120. https://doi.org/10.21273/HORTSCI.36.6.1117

10.21273/HORTSCI.36.6.1117
16

Grossnickle SC, MacDonald JE (2018) Seedling quality: History, application, and plant attributes. Forests 9:283. https://doi.org/10.3390/f9050283

10.3390/f9050283
17

Ha Y, Jeong J, Kwack Y (2024) Non-destructive detection of growth and quality in basil seedlings grown in a plant factory. J People Plants Environ 27:551-560. https://doi.org/10.11628/ksppe.2024.27.6.551

10.11628/ksppe.2024.27.6.551
18

Hyeon SJ, Yang HC, Kim YH, Bae YH, Jang DC (2024) Comparison of seedling quality of cucumber seedlings and growth and production after transplanting according to differences in seedling production systems. J Bio-Env Con 33:88-98. https://doi.org/10.12791/KSBEC.2024.33.2.088

10.12791/KSBEC.2024.33.2.088
19

Jacobs DF, Goodman RC, Gardiner ES, Salifu KF, Overton RP, Hernandez G (2012) Nursery stock quality as an indicator of bottomland hardwood forest restoration success in the lower mississippi river alluvial valley. Scandinavian Journal of Forest Research 27:255-269. https://doi.org/10.1080/02827581.2011.628948

10.1080/02827581.2011.628948
20

Jang DC, Kwon YW, Choi KY, Kim IS (2018) Comparison of growth characteristics fruit vegetable seedlings grown on cylindrical paper pot trays of plug trays. Protected Hortic Plant Fact 27:381-390. https://doi.org/10.12791/KSBEC.2018.27.4.381

10.12791/KSBEC.2018.27.4.381
21

Jeong HB, Cho MC, Yang EY (2021) Characteristics and utilization of major tomato cultivars developed by the Rural Development Administration. J Korean Soc Seed Sci Ind 17:10-16.

22

Jeong J, Ha Y, Kwack Y (2024) The selection of lettuce seedlings for transplanting in a plant factory by a non-destructive estimation of leaf area and fresh weight. Horticulturae 10:919. https://doi.org/10.3390/horticulturae10090919

10.3390/horticulturae10090919
23

Kim HM, Hwang SJ (2019) The growth and development of ‘Mini Chal’ tomato plug seedlings grown under various wavelengths using light emitting diodes. Agronomy 9:157. https://doi.org/10.3390/agronomy9030157

10.3390/agronomy9030157
24

KOSIS (2024a) General status of seedling companies. Available via https://kosis.kr/statHtml/statHtml.do?orgId=114&tblId=DT_114052_B01&conn_path=I2 Accessed 7 July 2026.

25

KOSIS (2024b) Production and sales of seedling companies by crop. Available via https://kosis.kr/statHtml/statHtml.do?orgId=114&tblId=DT_114052_B23_1&conn_path=I2 Accessed 7 July 2026.

26

Kronmal RA (1993) Spurious correlation and the fallacy of the ratio standard revisited. J R Stat Soc Ser A Stat Soc 156:379-392. https://doi.org/10.2307/2983064

10.2307/2983064
27

Leskovar DI, Cantliffe DJ, Stoffella PJ (1994) Transplant production systems influence growth and yield of fresh-market tomatoes. J Am Soc Hortic Sci 119:662-668. https://doi.org/10.21273/JASHS.119.4.662

10.21273/JASHS.119.4.662
28

Li C, Adhikari R, Yao Y, Miller AG, Kalbaugh K, Li D, Nemali K (2020) Measuring plant growth characteristics using smartphone based image analysis technique in controlled environment agriculture. Comput Electron Agric 168:105123. https://doi.org/10.1016/j.compag.2019.105123

10.1016/j.compag.2019.105123
29

Li L, Bie Z, Zhang Y, Huang Y, Peng C, Han B, Xu S (2026) Nondestructive detection of key phenotypes for the canopy of the watermelon plug seedlings based on deep learning. Hortic Plant J 12:149-160. https://doi.org/10.1016/j.hpj.2022.09.013

10.1016/j.hpj.2022.09.013
30

Lin K-H, Wu C-W, Chang Y-S (2018) Applying Dickson quality index, chlorophyll fluorescence, and leaf area index for assessing plant quality of Pentas lanceolata. Not Bot Horti Agrobot Cluj Napoca 47:169-176. https://doi.org/10.15835/nbha47111312

10.15835/nbha47111312
31

Lin T-T, Chien C-F, Liao W-C, Chung K-C, Chang J-M (2006) Machine vision systems for plant growth measurement and modeling. Environ Control Biol 44:181-187. https://doi.org/10.2525/ecb.44.181

10.2525/ecb.44.181
32

Liu X, Shi R, Gao M, He R, Li Y, Liu H (2023) Growth of tomato and cucumber seedlings under different light environments and their development after transplanting. Front Plant Sci 14:1164768. https://doi.org/10.3389/fpls.2023.1164768

10.3389/fpls.2023.116476837546262PMC10400448
33

Nkurunziza E, Nyalala S, Umuhoza KNJ (2022) Effect of seedling quality on growth, yield and quality of tomato (Solanum lycopersicum L.). Int J Hortic Sci 28:64-72. https://doi.org/10.31421/ijhs/28/2022/10836

10.31421/ijhs/28/2022/10836
34

Park JH, Kim SR, Mohammad FA, Lee JG, Emparang MCSC, Cho MG, Jeong DG, Kim MJ, Kang JS (2026) Effects of difference between day and night temperature (DIF) during the nursery stage on morphogenesis, post-transplant growth, and early yield of tomato. J Bio-Env Con 35:139-153. https://doi.org/10.12791/KSBEC.2026.35.2.139

10.12791/KSBEC.2026.35.2.139
35

Poorter H, Niinemets U, Poorter L, Wright IJ, Villar R (2009) Causes and consequences of variation in leaf mass per area (lma): A meta-analysis. New Phytol 182:565-588. https://doi.org/10.1111/j.1469-8137.2009.02830.x

10.1111/j.1469-8137.2009.02830.x
36

Qin K, Leskovar DI (2020) Humic substances improve vegetable seedling quality and post-transplant yield performance under stress conditions. Agriculture 10:254. https://doi.org/10.3390/agriculture10070254

10.3390/agriculture10070254
37

Rosado D, Ackermann A, Spassibojko O, Rossi M, Pedmale UV (2022) WRKY transcription factors and ethylene signaling modify root growth during the shade-avoidance response. Plant Physiol 188:1294-1311. https://doi.org/10.1093/plphys/kiab493

10.1093/plphys/kiab49334718759PMC8825332
38

Seo TC, An SW, Jang HW, Nam CW, Chun H, Kim YC, Kang TK, Lee SH (2018) An approach to determine the good seedling quality of grafted tomatoes (Solanum lycopersicum) grown in cylindrical paper pot through the relation analysis between DQI and short-term relative growth rate. J Bio-Env Con 27:302-311. https://doi.org/10.12791/KSBEC.2018.27.4.302

10.12791/KSBEC.2018.27.4.302
39

Syed TN, Liu J, Zhou X, Zhao S, Yuan Y, Mohamed SHA, Lakhiar IA (2019) Seedling-lump integrated non-destructive monitoring for automatic transplanting with intel realsense depth camera. Artif Intell Agric 3:18-32. https://doi.org/10.1016/j.aiia.2019.09.001

10.1016/j.aiia.2019.09.001
40

Tong JH, Li JB, Jiang HY (2013) Machine vision techniques for the evaluation of seedling quality based on leaf area. Biosyst Eng 115:369-379. https://doi.org/10.1016/j.biosystemseng.2013.02.006

10.1016/j.biosystemseng.2013.02.006
41

Yamaguchi N, Okumura H, Fukuda O, Yeoh WL, Tanaka M (2024) Estimating tomato plant leaf area using multiple images from different viewing angles. J Adv Comput Intell Intell Inform 28:352-360. https://doi.org/10.20965/jaciii.2024.p0352

10.20965/jaciii.2024.p0352
42

Yang S, Zheng L, Gao W, Wang B, Hao X, Mi J, Wang M (2020) An efficient processing approach for colored point cloud-based high-throughput seedling phenotyping. Remote Sens 12:1540. https://doi.org/10.3390/rs12101540

10.3390/rs12101540
Information
  • Publisher :The Korean Society for Bio-Environment Control
  • Publisher(Ko) :(사)한국생물환경조절학회
  • Journal Title :Journal of Bio-Environment Control
  • Journal Title(Ko) :생물환경조절학회지
  • Volume : 35
  • No :3
  • Pages :274-285
  • Received Date : 2026-07-16
  • Revised Date : 2026-07-29
  • Accepted Date : 2026-07-29
  • DOI :https://doi.org/10.12791/KSBEC.2026.35.3.274
Journal Informaiton Journal of Bio-Environment Control Journal of Bio-Environment Control
Online Submission
  • NRF
  • KOFST
  • crossref crossmark
  • crossref cited-by
  • crossref funder-registry
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close